Hospital-associated (HA) infections are a major concern in the medical community. It is estimated that at any one time, 9% of all hospitalized patients have an infection acquired while in the hospital. The human and economic costs of these infections are huge. The CDC estimates that nearly 2 million patients annually are affected by a HA-infection and about 88,000 die as a direct or indirect consequence of their infection. Resulting in prolonged hospitalizations, HA-infections add nearly $5 billion to U.S. health costs every year. HA-infections are an important cause of morbidity and mortality, especially for children and ICU populations. Common HA-infections are catheter-related blood stream infections, ventilator associated pneumonia, respiratory viruses, urinary track infections, and rotavirus. For each individual infection or virus, it is well-known what the best practices are to reduce their occurrence. Unfortunately, despite the evidence-based interventions, prolonged hospital stays and even deaths do occur. Since a mechanism to understand the dynamics of a system oriented towards abating infection transmission is unavailable, the goal for the proposed research is to develop such a model. We propose an integrated approach concentrating on the aforementioned infections in the pediatric, cardiac, and neo-natal ICUs. The goal of this work is to develop an engineering-based, systems-level methodology that models the likelihood of infection transmission within pediatric ICUs. The model will strategically use systems engineering tools to measure, characterize, and optimize the environment to improve performance by reducing infection rates. The approach is novel because in addition to modeling the physical and methodological aspects of the ICU environment, it will combine cognitive theories, nonlinear statistical algorithms, and risk analysis models. The proposed research is a collaborative effort between the departments of Industrial Engineering and Pediatrics at the University of Washington and the Children's Hospital and Regional Medical Center, Seattle. [unreadable] [unreadable] [unreadable]